An analytical approach to confidence interval estimation of river microplastic sampling

Area of Science:

• Environmental Science
• Analytical Chemistry
• Ecotoxicology

Background:

• Microplastics (MPs) are pervasive pollutants in freshwater ecosystems.
• Rivers are key transport pathways for MPs, necessitating effective monitoring.
• Standardized sampling methods and error evaluation for freshwater MPs are lacking.

Purpose of the Study:

• To develop a novel method for calculating confidence intervals (CIs) for microplastic numerical concentration from single samples.
• To validate the proposed CI framework using real-world data from urban rivers.
• To provide a standardized approach for comparing microplastic data across different studies.

Main Methods:

• Proposed a framework based on the Poisson point process to compute CIs for MP concentration (particles·m⁻³).
• Validated the method using microplastic samples collected from two urban rivers in Chiba, Japan.
• Employed random number simulations to assess the applicability and accuracy of the CIs.

Main Results:

• The Poisson point process framework provides reliable CIs for microplastic concentration estimates.
• The method is applicable when at least 10 microplastics are present in a sample.
• With ≥50 microplastics, the sampling error (95% CI) was within ±30% of the estimated concentration.

Conclusions:

• The proposed framework enables intercomparison of microplastic data from single river samples without replicates.
• Sampling error in microplastic monitoring can be reduced by increasing the volume of river water sampled.
• This method addresses a critical gap in standardized microplastic assessment in freshwater environments.